





'''simple docstring'''






import argparse
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import BeitConfig, BeitForImageClassification, BeitForMaskedImageModeling, BeitImageProcessor
from transformers.image_utils import PILImageResampling
from transformers.utils import logging


logging.set_verbosity_info()
lowerCAmelCase									=   logging.get_logger(__name__)






def   __A    (				a_    :	List[str]   ,a_    :	int=False   ,a_    :	Optional[Any]=False						):
		lowerCAmelCase  :     List[Any]       						= "backbone." if is_semantic else ""

		lowerCAmelCase  :     str       						= []
		for i in range(config.num_hidden_layers						):
				# encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
				rename_keys.append((f'''{prefix}blocks.{i}.norm1.weight''', f'''beit.encoder.layer.{i}.layernorm_before.weight''')						)
				rename_keys.append((f'''{prefix}blocks.{i}.norm1.bias''', f'''beit.encoder.layer.{i}.layernorm_before.bias''')						)
				rename_keys.append(
				    (f'''{prefix}blocks.{i}.attn.proj.weight''', f'''beit.encoder.layer.{i}.attention.output.dense.weight''')						)
				rename_keys.append(
				    (f'''{prefix}blocks.{i}.attn.proj.bias''', f'''beit.encoder.layer.{i}.attention.output.dense.bias''')						)
				rename_keys.append((f'''{prefix}blocks.{i}.norm2.weight''', f'''beit.encoder.layer.{i}.layernorm_after.weight''')						)
				rename_keys.append((f'''{prefix}blocks.{i}.norm2.bias''', f'''beit.encoder.layer.{i}.layernorm_after.bias''')						)
				rename_keys.append((f'''{prefix}blocks.{i}.mlp.fc1.weight''', f'''beit.encoder.layer.{i}.intermediate.dense.weight''')						)
				rename_keys.append((f'''{prefix}blocks.{i}.mlp.fc1.bias''', f'''beit.encoder.layer.{i}.intermediate.dense.bias''')						)
				rename_keys.append((f'''{prefix}blocks.{i}.mlp.fc2.weight''', f'''beit.encoder.layer.{i}.output.dense.weight''')						)
				rename_keys.append((f'''{prefix}blocks.{i}.mlp.fc2.bias''', f'''beit.encoder.layer.{i}.output.dense.bias''')						)

		# projection layer + position embeddings
		rename_keys.extend(
		    [
		        (f'''{prefix}cls_token''', "beit.embeddings.cls_token"),
		        (f'''{prefix}patch_embed.proj.weight''', "beit.embeddings.patch_embeddings.projection.weight"),
		        (f'''{prefix}patch_embed.proj.bias''', "beit.embeddings.patch_embeddings.projection.bias"),
		        (f'''{prefix}pos_embed''', "beit.embeddings.position_embeddings"),
		    ]						)

		if has_lm_head:
				# mask token + layernorm
				rename_keys.extend(
				    [
				        ("mask_token", "beit.embeddings.mask_token"),
				        ("norm.weight", "layernorm.weight"),
				        ("norm.bias", "layernorm.bias"),
				    ]						)
		else:
				# layernorm + classification head
				rename_keys.extend(
				    [
				        ("fc_norm.weight", "beit.pooler.layernorm.weight"),
				        ("fc_norm.bias", "beit.pooler.layernorm.bias"),
				        ("head.weight", "classifier.weight"),
				        ("head.bias", "classifier.bias"),
				    ]						)

		return rename_keys






def   __A    (				a_    :	Dict   ,a_    :	Union[str, Any]   ,a_    :	str=False   ,a_    :	Any=False						):
		for i in range(config.num_hidden_layers						):
				lowerCAmelCase  :     int       						= "backbone." if is_semantic else ""
				# queries, keys and values
				lowerCAmelCase  :     Any       						= state_dict.pop(f'''{prefix}blocks.{i}.attn.qkv.weight'''						)
				lowerCAmelCase  :     int       						= state_dict.pop(f'''{prefix}blocks.{i}.attn.q_bias'''						)
				lowerCAmelCase  :     Union[str, Any]       						= state_dict.pop(f'''{prefix}blocks.{i}.attn.v_bias'''						)

				lowerCAmelCase  :     Any       						= in_proj_weight[
				    : config.hidden_size, :
				]
				lowerCAmelCase  :     List[Any]       						= q_bias
				lowerCAmelCase  :     str       						= in_proj_weight[
				    config.hidden_size : config.hidden_size * 2, :
				]
				lowerCAmelCase  :     Tuple       						= in_proj_weight[
				    -config.hidden_size :, :
				]
				lowerCAmelCase  :     Optional[Any]       						= v_bias

				# gamma_1 and gamma_2
				# we call them lambda because otherwise they are renamed when using .from_pretrained
				lowerCAmelCase  :     Union[str, Any]       						= state_dict.pop(f'''{prefix}blocks.{i}.gamma_1'''						)
				lowerCAmelCase  :     Any       						= state_dict.pop(f'''{prefix}blocks.{i}.gamma_2'''						)

				lowerCAmelCase  :     Optional[int]       						= gamma_a
				lowerCAmelCase  :     List[str]       						= gamma_a






def   __A    (				a_    :	int   ,a_    :	str   ,a_    :	Any						):
		lowerCAmelCase  :     int       						= dct.pop(a_						)
		lowerCAmelCase  :     Optional[Any]       						= val






def   __A    (				):
		lowerCAmelCase  :     List[Any]       						= "http://images.cocodataset.org/val2017/000000039769.jpg"
		lowerCAmelCase  :     str       						= Image.open(requests.get(a_   ,stream=a_						).raw						)
		return im






@torch.no_grad()
def   __A    (				a_    :	List[Any]   ,a_    :	Tuple   ,a_    :	Dict=False						):
		lowerCAmelCase  :     str       						= False if "rvlcdip" in checkpoint_url else True
		lowerCAmelCase  :     List[Any]       						= BeitConfig(use_absolute_position_embeddings=a_   ,use_mask_token=a_						)

		# size of the architecture
		if "large" in checkpoint_url or "dit-l" in checkpoint_url:
				lowerCAmelCase  :     Dict       						= 1_0_2_4
				lowerCAmelCase  :     Union[str, Any]       						= 4_0_9_6
				lowerCAmelCase  :     Union[str, Any]       						= 2_4
				lowerCAmelCase  :     Optional[Any]       						= 1_6

		# labels
		if "rvlcdip" in checkpoint_url:
				lowerCAmelCase  :     Tuple       						= 1_6
				lowerCAmelCase  :     Union[str, Any]       						= "huggingface/label-files"
				lowerCAmelCase  :     Any       						= "rvlcdip-id2label.json"
				lowerCAmelCase  :     Optional[Any]       						= json.load(open(hf_hub_download(a_   ,a_   ,repo_type="dataset"						)   ,"r"						)						)
				lowerCAmelCase  :     Tuple       						= {int(a_						): v for k, v in idalabel.items()}
				lowerCAmelCase  :     Any       						= idalabel
				lowerCAmelCase  :     str       						= {v: k for k, v in idalabel.items()}

		# load state_dict of original model, remove and rename some keys
		lowerCAmelCase  :     str       						= torch.hub.load_state_dict_from_url(a_   ,map_location="cpu"						)["model"]

		lowerCAmelCase  :     Any       						= create_rename_keys(a_   ,has_lm_head=a_						)
		for src, dest in rename_keys:
				rename_key(a_   ,a_   ,a_						)
		read_in_q_k_v(a_   ,a_   ,has_lm_head=a_						)

		# load HuggingFace model
		lowerCAmelCase  :     Union[str, Any]       						= BeitForMaskedImageModeling(a_						) if has_lm_head else BeitForImageClassification(a_						)
		model.eval()
		model.load_state_dict(a_						)

		# Check outputs on an image
		lowerCAmelCase  :     List[str]       						= BeitImageProcessor(
		    size=config.image_size   ,resample=PILImageResampling.BILINEAR   ,do_center_crop=a_						)
		lowerCAmelCase  :     Tuple       						= prepare_img()

		lowerCAmelCase  :     Dict       						= image_processor(images=a_   ,return_tensors="pt"						)
		lowerCAmelCase  :     Optional[Any]       						= encoding["pixel_values"]

		lowerCAmelCase  :     Any       						= model(a_						)
		lowerCAmelCase  :     Dict       						= outputs.logits

		# verify logits
		lowerCAmelCase  :     int       						= [1, 1_6] if "rvlcdip" in checkpoint_url else [1, 1_9_6, 8_1_9_2]
		assert logits.shape == torch.Size(a_						), "Shape of logits not as expected"

		Path(a_						).mkdir(exist_ok=a_						)
		print(f'''Saving model to {pytorch_dump_folder_path}'''						)
		model.save_pretrained(a_						)
		print(f'''Saving image processor to {pytorch_dump_folder_path}'''						)
		image_processor.save_pretrained(a_						)

		if push_to_hub:
				if has_lm_head:
						lowerCAmelCase  :     List[Any]       						= "dit-base" if "base" in checkpoint_url else "dit-large"
				else:
						lowerCAmelCase  :     Dict       						= "dit-base-finetuned-rvlcdip" if "dit-b" in checkpoint_url else "dit-large-finetuned-rvlcdip"
				image_processor.push_to_hub(
				    repo_path_or_name=Path(a_   ,a_						)   ,organization="nielsr"   ,commit_message="Add image processor"   ,use_temp_dir=a_   ,)
				model.push_to_hub(
				    repo_path_or_name=Path(a_   ,a_						)   ,organization="nielsr"   ,commit_message="Add model"   ,use_temp_dir=a_   ,)


if __name__ == "__main__":
					lowerCAmelCase									=   argparse.ArgumentParser()

					parser.add_argument(
					    """--checkpoint_url""",
					    default="""https://layoutlm.blob.core.windows.net/dit/dit-pts/dit-base-224-p16-500k-62d53a.pth""",
					    type=str,
					    help="""URL to the original PyTorch checkpoint (.pth file).""",
					)
					parser.add_argument(
					    """--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the folder to output PyTorch model."""
					)
					parser.add_argument(
					    """--push_to_hub""",
					    action="""store_true""",
					)
					lowerCAmelCase									=   parser.parse_args()
					convert_dit_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub)










'''simple docstring'''






import json
import os
import shutil
import warnings
from argparse import ArgumentParser, Namespace
from pathlib import Path
from typing import List

from ..utils import logging
from . import BaseTransformersCLICommand


try:
					from cookiecutter.main import cookiecutter

					lowerCAmelCase									=   True
except ImportError:
					lowerCAmelCase									=   False

lowerCAmelCase									=   logging.get_logger(__name__)  # pylint: disable=invalid-name






def   __A    (				a_    :	Namespace						):
		return AddNewModelCommand(args.testing   ,args.testing_file   ,path=args.path						)

class 		lowerCamelCase	(   _A							):



	@staticmethod
	def 	_lowerCamelCase     ( a_						):
			lowerCAmelCase  :     Optional[int]       						= parser.add_parser("add-new-model"						)
			add_new_model_parser.add_argument("--testing"		,   action="store_true"		,   help="If in testing mode."						)
			add_new_model_parser.add_argument("--testing_file"		,   type=a_		,   help="Configuration file on which to run."						)
			add_new_model_parser.add_argument(
			    "--path"		,   type=a_		,   help="Path to cookiecutter. Should only be used for testing purposes."						)
			add_new_model_parser.set_defaults(func=a_						)



	def __init__( self		,   a_		,   a_		,   a_=None		,   *a_						):
			lowerCAmelCase  :     Any       						= testing
			lowerCAmelCase  :     str       						= testing_file
			lowerCAmelCase  :     Optional[int]       						= path





	def 	_lowerCamelCase     ( self						):
			warnings.warn(
			    "The command `transformers-cli add-new-model` is deprecated and will be removed in v5 of Transformers. "
			    "It is not actively maintained anymore, so might give a result that won't pass all tests and quality "
			    "checks, you should use `transformers-cli add-new-model-like` instead."						)
			if not _has_cookiecutter:
					raise ImportError(
					    "Model creation dependencies are required to use the `add_new_model` command. Install them by running "
					    "the following at the root of your `transformers` clone:\n\n\t$ pip install -e .[modelcreation]\n"						)
			# Ensure that there is no other `cookiecutter-template-xxx` directory in the current working directory
			lowerCAmelCase  :     Optional[Any]       						= [directory for directory in os.listdir() if "cookiecutter-template-" == directory[:22]]
			if len(a_						) > 0:
					raise ValueError(
					    "Several directories starting with `cookiecutter-template-` in current working directory. "
					    "Please clean your directory by removing all folders starting with `cookiecutter-template-` or "
					    "change your working directory."						)

			lowerCAmelCase  :     List[str]       						= (
			    Path(a_						).parent.parent.parent.parent if self._path is None else Path(self._path						).parent.parent
			)
			lowerCAmelCase  :     str       						= path_to_transformer_root / "templates" / "adding_a_new_model"

			# Execute cookiecutter
			if not self._testing:
					cookiecutter(str(a_						)						)
			else:
					with open(self._testing_file		,   "r"						) as configuration_file:
							lowerCAmelCase  :     Dict       						= json.load(a_						)

					cookiecutter(
					    str(path_to_cookiecutter if self._path is None else self._path						)		,   no_input=a_		,   extra_context=a_		,   )

			lowerCAmelCase  :     Optional[Any]       						= [directory for directory in os.listdir() if "cookiecutter-template-" in directory[:22]][0]

			# Retrieve configuration
			with open(directory + "/configuration.json"		,   "r"						) as configuration_file:
					lowerCAmelCase  :     str       						= json.load(a_						)

			lowerCAmelCase  :     List[str]       						= configuration["lowercase_modelname"]
			lowerCAmelCase  :     List[Any]       						= configuration["generate_tensorflow_pytorch_and_flax"]
			os.remove(F'''{directory}/configuration.json'''						)

			lowerCAmelCase  :     Optional[int]       						= "PyTorch" in generate_tensorflow_pytorch_and_flax
			lowerCAmelCase  :     Optional[int]       						= "TensorFlow" in generate_tensorflow_pytorch_and_flax
			lowerCAmelCase  :     Optional[Any]       						= "Flax" in generate_tensorflow_pytorch_and_flax

			lowerCAmelCase  :     List[str]       						= F'''{path_to_transformer_root}/src/transformers/models/{lowercase_model_name}'''
			os.makedirs(a_		,   exist_ok=a_						)
			os.makedirs(F'''{path_to_transformer_root}/tests/models/{lowercase_model_name}'''		,   exist_ok=a_						)

			# Tests require submodules as they have parent imports
			with open(F'''{path_to_transformer_root}/tests/models/{lowercase_model_name}/__init__.py'''		,   "w"						):
					pass

			shutil.move(
			    F'''{directory}/__init__.py'''		,   F'''{model_dir}/__init__.py'''		,   )
			shutil.move(
			    F'''{directory}/configuration_{lowercase_model_name}.py'''		,   F'''{model_dir}/configuration_{lowercase_model_name}.py'''		,   )

			def remove_copy_lines(a_						):
					with open(a_		,   "r"						) as f:
							lowerCAmelCase  :     Dict       						= f.readlines()
					with open(a_		,   "w"						) as f:
							for line in lines:
									if "# Copied from transformers." not in line:
											f.write(a_						)

			if output_pytorch:
					if not self._testing:
							remove_copy_lines(F'''{directory}/modeling_{lowercase_model_name}.py'''						)

					shutil.move(
					    F'''{directory}/modeling_{lowercase_model_name}.py'''		,   F'''{model_dir}/modeling_{lowercase_model_name}.py'''		,   )

					shutil.move(
					    F'''{directory}/test_modeling_{lowercase_model_name}.py'''		,   F'''{path_to_transformer_root}/tests/models/{lowercase_model_name}/test_modeling_{lowercase_model_name}.py'''		,   )
			else:
					os.remove(F'''{directory}/modeling_{lowercase_model_name}.py'''						)
					os.remove(F'''{directory}/test_modeling_{lowercase_model_name}.py'''						)

			if output_tensorflow:
					if not self._testing:
							remove_copy_lines(F'''{directory}/modeling_tf_{lowercase_model_name}.py'''						)

					shutil.move(
					    F'''{directory}/modeling_tf_{lowercase_model_name}.py'''		,   F'''{model_dir}/modeling_tf_{lowercase_model_name}.py'''		,   )

					shutil.move(
					    F'''{directory}/test_modeling_tf_{lowercase_model_name}.py'''		,   F'''{path_to_transformer_root}/tests/models/{lowercase_model_name}/test_modeling_tf_{lowercase_model_name}.py'''		,   )
			else:
					os.remove(F'''{directory}/modeling_tf_{lowercase_model_name}.py'''						)
					os.remove(F'''{directory}/test_modeling_tf_{lowercase_model_name}.py'''						)

			if output_flax:
					if not self._testing:
							remove_copy_lines(F'''{directory}/modeling_flax_{lowercase_model_name}.py'''						)

					shutil.move(
					    F'''{directory}/modeling_flax_{lowercase_model_name}.py'''		,   F'''{model_dir}/modeling_flax_{lowercase_model_name}.py'''		,   )

					shutil.move(
					    F'''{directory}/test_modeling_flax_{lowercase_model_name}.py'''		,   F'''{path_to_transformer_root}/tests/models/{lowercase_model_name}/test_modeling_flax_{lowercase_model_name}.py'''		,   )
			else:
					os.remove(F'''{directory}/modeling_flax_{lowercase_model_name}.py'''						)
					os.remove(F'''{directory}/test_modeling_flax_{lowercase_model_name}.py'''						)

			shutil.move(
			    F'''{directory}/{lowercase_model_name}.md'''		,   F'''{path_to_transformer_root}/docs/source/en/model_doc/{lowercase_model_name}.md'''		,   )

			shutil.move(
			    F'''{directory}/tokenization_{lowercase_model_name}.py'''		,   F'''{model_dir}/tokenization_{lowercase_model_name}.py'''		,   )

			shutil.move(
			    F'''{directory}/tokenization_fast_{lowercase_model_name}.py'''		,   F'''{model_dir}/tokenization_{lowercase_model_name}_fast.py'''		,   )

			from os import fdopen, remove
			from shutil import copymode, move
			from tempfile import mkstemp

			def replace(a_		,   a_		,   a_						):
					# Create temp file
					lowerCAmelCase						,		lowerCAmelCase  :     Tuple       						= mkstemp()
					lowerCAmelCase  :     int       						= False
					with fdopen(a_		,   "w"						) as new_file:
							with open(a_						) as old_file:
									for line in old_file:
											new_file.write(a_						)
											if line_to_copy_below in line:
													lowerCAmelCase  :     Optional[int]       						= True
													for line_to_copy in lines_to_copy:
															new_file.write(a_						)

					if not line_found:
							raise ValueError(F'''Line {line_to_copy_below} was not found in file.'''						)

					# Copy the file permissions from the old file to the new file
					copymode(a_		,   a_						)
					# Remove original file
					remove(a_						)
					# Move new file
					move(a_		,   a_						)

			def skip_units(a_						):
					return (
					    ("generating PyTorch" in line and not output_pytorch)
					    or ("generating TensorFlow" in line and not output_tensorflow)
					    or ("generating Flax" in line and not output_flax)
					)

			def replace_in_files(a_						):
					with open(a_						) as datafile:
							lowerCAmelCase  :     Dict       						= []
							lowerCAmelCase  :     Optional[int]       						= False
							lowerCAmelCase  :     List[Any]       						= False
							for line in datafile:
									if "# To replace in: " in line and "##" not in line:
											lowerCAmelCase  :     Optional[int]       						= line.split("\""						)[1]
											lowerCAmelCase  :     Tuple       						= skip_units(a_						)
									elif "# Below: " in line and "##" not in line:
											lowerCAmelCase  :     Any       						= line.split("\""						)[1]
											lowerCAmelCase  :     List[str]       						= skip_units(a_						)
									elif "# End." in line and "##" not in line:
											if not skip_file and not skip_snippet:
													replace(a_		,   a_		,   a_						)

											lowerCAmelCase  :     List[str]       						= []
									elif "# Replace with" in line and "##" not in line:
											lowerCAmelCase  :     Any       						= []
									elif "##" not in line:
											lines_to_copy.append(a_						)

					remove(a_						)

			replace_in_files(F'''{directory}/to_replace_{lowercase_model_name}.py'''						)
			os.rmdir(a_						)



